Local Polynomial Mixed-Effects Models for Longitudinal Data

By Hulin Wu, Jin-Ting Zhang. 2002

150 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Statistics and Probability

View Paper: https://www.semanticscholar.org/paper/b76e836488db1b2fdb0de14c02775103ee67e882

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Citations in the corpus

Nonparametric Regression Analysis of Longitudinal Data (2005, background): “They are usually referred to as ”longitudinal data” in biomedical applications, where a small number of repeated measurements, ni, over time per subject is common, and as ”functional data” in engineering and biological applications, where ni is often large.”
Functional Regression (2014, unknown): “Wu & Zhang (2002) used local polynomials to represent the mean function and curve-level random effect functions, with independent but heteroscedastic residual errors.”
Semiparametric estimation in general repeated measures problems (2006, methodology): “This problem has a large literature, with many kernel-based methods (Zeger and Diggle (1994), Hoover et al. (1998), Lin and Ying (2001), Wu and Zhang (2002) and many others), all of them estimating θ0.”
Efficient Estimation in Marginal Partially Linear Models for Longitudinal/Clustered Data Using Splines (2007, unknown): “…Brumback & Rice (1998), Staniswalis & Lee (1998), Hoover et al. (1998), Wu et al. (1998), Fan & Zhang (2000), Lin & Carroll (2000), Chiang et al. (2001), Lin & Ying (2001), Martinussen & Scheike (2001), Wu & Zhang (2002), Wu & Liang (2004), Fan & Li (2004), Sun & Wu (2005), among many others.”
Asymptotic Equivalence between Cross-Validations and Akaike Information Criteria in Mixed-Effects Models (2021, methodology): “Abstract: For model selection in mixed effects models, Vaida and Blanchard (2005) demonstrated that the marginal Akaike information criterion is appropriate as to the questions regarding the population and the conditional Akaike information criterion is appropriate as to the questions regarding the ”
Estimation of the covariance matrix of random effects in longitudinal studies (2007, methodology): “EFFECTS IN LONGITUDINAL STUDIES By Yan Sun, Wenyang Zhang and Howell Tong”
Cross-Validation for Correlated Data (2019, background): “Another CV variant that is relevant for a balanced longitudinal data setting is leave observation from each cluster out (LOFCO, Wu and Zhang 2002).”
Model Selection in Linear Mixed Models (2013, background): “The conditional expectations can therefore be written in a form which is linear in the parameters that are subject to selection. Bondell, Krishna and Ghosh (2010) and Ibrahim et al.”